JOURNAL ARTICLE
Covalent Deposition of Diels–Alder Reaction on Polyester Fiber Surface and Its Enhancement Mechanism on SBS‐Modified Asphalt.
Published In: Journal of Applied Polymer Science, 2025, v. 142, n. 15. P. 1 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Ren, Denghui; Fan, Shencheng; He, Lu; Liu, Yu; Deng, Shilin; Luo, Yating; Wang, Peihui; Jin, Xin; Li, Jing 3 of 3
Abstract
Polyester (PET) fibers offer excellent chemical stability, mechanical strength, and high‐temperature tolerance, making them promising for asphalt applications. However, their smooth, chemically inert surface limits interfacial bonding with asphalt, reducing the overall performance. To address this, a mild covalent deposition method was employed, utilizing a polydopamine and furfuryl amine crosslinking reaction to incorporate furan groups on PET fibers. This enabled the formation of a dense Diels–Alder (D–A) reactive crosslinking layer. Characterization revealed that this layer increased surface roughness, free energy, and active functional groups, improving wetting and interlocking with asphalt. Rheological tests showed enhanced performance in styrene–butadiene–styrene (SBS)‐modified asphalt composites, with a 7.73% increase in G* at 46°C and an 81.94% improvement in adhesion. Molecular dynamics simulations confirmed the enhanced interfacial interactions between DA‐PET fibers and SBS‐modified asphalt, providing insight into the mechanism behind the improvements. This study presents a novel approach for optimizing PET fiber performance in asphalt materials. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Applied Polymer Science. 2025/04, Vol. 142, Issue 15, p1
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2025
- ISSN:00218995
- DOI:10.1002/app.56712
- Accession Number:183845627
- Copyright Statement:Copyright of Journal of Applied Polymer Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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